181 citations · 1.2k across the 31 of their papers we have counts for
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Assessment of First-Principles Methods in Modeling the Melting Properties of Water
Yifan Li, Bingjia Yang, Chunyi Zhang +5
First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on t…
Training Dataset Matters for Machine Learning Interatomic Potentials: Water Simulation as an Example
Yifan Li, Bingjia Yang, Chunyi Zhang +5
Accurate predictions of machine learning interatomic potentials (MLIPs) rely on training datasets covering relevant thermodynamic conditions and phases. Using water as an example,…
Hybrid ab initio and empirical machine learning models for the potential energy surface
Pablo Peña-Cano, Pablo M. Piaggi
We propose a methodology to generate hybrid machine learning models for the potential energy surface trained simultaneously on data from ab initio electronic structure calculations…
Molecular mechanism of ice nucleation on feldspar
Wanqi Zhou, Pablo M. Piaggi
Understanding how water transforms into ice at complex surfaces is central to a wide range of natural and technological processes, yet molecular simulations of this transformation…
Early Stages of Self-Healing at Tungsten Grain Boundaries from Ab Initio Machine Learning Simulations
Jorge Suárez-Recio, Pablo M. Piaggi, Francisco J. Domínguez-Gutiérrez +2
Nanostructured tungsten has been reported as a possible alternative plasma-facing material due to its potential ability to self-heal radiation-induced defects, a property that is a…
On the possibility of chiral symmetry breaking in liquid hydrogen peroxide
Roberto Menta, Pablo G. Debenedetti, Roberto Car +1
Molecular chirality is a key concept in chemistry with implications for the origin of life and the manufacturing of pharmaceuticals. Previous simulations of a chiral molecular mode…